Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling

The authors present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, they aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, they use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, they adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. The authors' demonstrate their approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data.


  • English

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  • Accession Number: 01860129
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 30 2022 2:27PM